Course Overview

This introductory course on data science covers fundamental concepts and techniques in the field. By the end of this course, students will be able to:

• Understand key data science concepts and techniques
• Implement basic data science algorithms
• Evaluate and compare model performance
• Apply data science techniques to real-world problems

Prerequisites

• Basic knowledge of linear algebra and calculus
• Programming experience in Python
• Probability and statistics fundamentals

Textbooks

• Primary: “Python Data Science Handbook” by Jake VanderPlas
• Reference: “Hands-On Machine Learning” by Aurélien Géron

Grading

• Assignments: 40%
• Final Project: 50%
• Participation: 10%